Comparison of the number of victims of various crimes in each state in the same year
Rate of change in the number of victims of various crimes by state
| State | robbery_rate | robbery_change | Sexual assault_rate | Sexual assault_change | Murder_rate | Murder_change |
|---|---|---|---|---|---|---|
| Capital | -19.867550 | -30 | 69.430052 | 134 | -100.000000 | -3 |
| NewSouthWales | -43.953488 | -945 | 51.451369 | 3740 | 4.109589 | 3 |
| Northern Territory | 43.478261 | 20 | 7.926829 | 26 | -9.090909 | -1 |
| Queensland | 73.246753 | 564 | 14.626091 | 620 | -2.083333 | -1 |
| South Australia | -34.482759 | -180 | 13.719736 | 187 | -33.333333 | -5 |
| Tasmania | 1.176471 | 1 | 9.604520 | 17 | -33.333333 | -2 |
| Victoria | 40.751043 | 586 | 60.622761 | 2200 | 19.148936 | 9 |
| Western Australia | 13.358071 | 72 | 67.412334 | 1115 | -10.000000 | -3 |
| State | extortion_rate | extortion_change | Manslaughter_rate | Manslaughter_change | Kidnapping_rate | Kidnapping_change |
|---|---|---|---|---|---|---|
| Capital | Inf | 6 | NaN | 0 | Inf | 7 |
| NewSouthWales | -46.24277 | -80 | 18.18182 | 2 | -31.610942 | -104 |
| Northern Territory | Inf | 3 | -100.00000 | -3 | NaN | 0 |
| Queensland | 108.69565 | 50 | -57.14286 | -4 | -13.235294 | -9 |
| South Australia | 96.77419 | 30 | NaN | 0 | -9.230769 | -6 |
| Tasmania | NaN | 0 | NaN | 0 | Inf | 3 |
| Victoria | 43.06569 | 59 | 366.66667 | 11 | 36.206897 | 42 |
| Western Australia | 20.00000 | 18 | 0.00000 | 0 | 21.052632 | 4 |
The rate of change in the rate of various crime victims by state
| State | 2010-2011 | rate_change |
|---|---|---|
| NewSouthWales | 34.4 | 33.8249754 |
| Victoria | 21.9 | 32.9323308 |
| Queensland | -0.8 | -0.8316008 |
| South Australia | 4.7 | 5.6085919 |
| Western Australia | 33.4 | 46.2603878 |
| Tasmania | 1.5 | 4.3103448 |
| Northern Territory | 23.3 | 43.7148218 |
| Capital | 23.3 | 43.7148218 |
On the whole, it shows an upward trend year by year recently.
The number of female victims is much higher than that of male victims, almost twice.
With the exception of sexual assault and kidnapping, the majority of victims are men.
The vast majority of cases are sexual assaults.
The number of robberies among the remaining crime categories is also considerable. (Including both armed and unarmed)
Possible reasons:
According to statistics of gender-specific crimes in various countries, male victims are the majority in almost all crimes except for sex-related crimes in which more women are victims.
| Gender | Year | sa_rate |
|---|---|---|
| Female | 2010 | 81.14353 |
| Female | 2011 | 80.92590 |
| Female | 2012 | 81.71220 |
| Female | 2013 | 83.46100 |
| Female | 2014 | 85.77439 |
| Female | 2015 | 87.63073 |
| Female | 2016 | 87.29571 |
| Female | 2017 | 88.86702 |
| Female | 2018 | 88.74093 |
| Female | 2019 | 87.99638 |
| Male | 2010 | 21.67991 |
| Male | 2011 | 23.79634 |
| Male | 2012 | 27.87459 |
| Male | 2013 | 30.70995 |
| Male | 2014 | 34.46328 |
| Male | 2015 | 38.35306 |
| Male | 2016 | 38.22735 |
| Male | 2017 | 39.55563 |
| Male | 2018 | 35.52154 |
| Male | 2019 | 33.06397 |
Take sexual assault, female victims of sexual assault make up a very high proportion of total female victims.
ABS personal safety survey also measures the number of women who contacted the police about the most recent incident within the last 10 years. Only 13.4% of women did so.
---
title: "Report"
author:
- familyname: Wang
othernames: Yiru
address: Monash Universtidy
email: "ywan0553@student.monash.edu"
correspondingauthor: true
qualifications: section1
- familyname: Xu
othernames: Kexin
address: Monash University
email: "kxuu0029@student.monash.edu"
qualifications: section2
correspondingauthor: true
- familyname: Tang
othernames: Ruiqi
address: Monash University
email: "rtan00062student.monash.edu"
correspondingauthor: true
qualifications: section3
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
source_code: embed
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.align = "center")
```
```{r, echo = FALSE, message = FALSE, warning = FALSE}
# Libraries
library(flexdashboard)
library(tinytex)
library(gridExtra)
library(tidyverse)
library(readr)
library(bookdown)
library(knitr)
library(plotly)
library(kableExtra)
library(readxl)
```
State {data-icon="fa-globe"}
=============================
Row {data-height=1000}
-------------------------------------
### Chart 1
```{r NewSouthWales}
NewSouthWales <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A5:AB30")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "NewSouthWales")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r NewSouthWales-rate,message=FALSE}
NewSouthWales_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "NewSouthWales")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r Victoria}
Victoria <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A5:AB28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Victoria")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r Victoria_rate,message=FALSE}
Victoria_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A29:AB37")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Victoria")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r Queensland}
Queensland <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A5:AB28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Queensland")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r Queensland_rate,message=FALSE}
Queensland_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A29:AB37")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Queensland")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r South_Australia}
South_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A5:AB30")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "South Australia")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r South_Australia_rate,message=FALSE}
South_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "South Australia")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r Western_Australia}
Western_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A5:AB30")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Western Australia")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r Western_Australia_rate,message=FALSE}
Western_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Western Australia")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r Tasmania1}
Tasmania1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls",
sheet = 8,range = "A5:AB22")%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))%>%
select(!("1993":"2009"))%>%
pivot_longer(cols = '2010':'2013',
names_to = "Year",
values_to = "Count") %>%
mutate(Count = as.double(Count))%>%
mutate(State = "Tasmania")%>%
select(Offence,Year,Count,State)
```
```{r Tasmania}
Tasmania2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls",
sheet = 8,range = "A5:AB22")%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))%>%
select(!("1993":"2013"))%>%
pivot_longer(cols = '2014':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Tasmania")%>%
select(Offence,Year,Count,State)
Tasmania <- bind_rows(Tasmania1,Tasmania2)
```
```{r Tasmania_rate1,message=FALSE}
Tasmania_rate1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
pivot_longer(cols = '2010':'2013',
names_to = "Year",
values_to = "Rate") %>%
mutate(Rate = as.double(Rate))%>%
mutate(State = "Tasmania")%>%
select(Offence,Year,Rate,State)
```
```{r Tasmania_rate,message=FALSE}
Tasmania_rate2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
pivot_longer(cols = '2014':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Tasmania")%>%
select(Offence,Year,Rate,State)
Tasmania_rate <- bind_rows(Tasmania_rate1,Tasmania_rate2)
```
```{r Northern_Territory}
Northern_Territory <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 9,range = "A5:AB30")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Northern Territory")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r Northern_Territory_rate,message=FALSE}
Northern_Territory_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Northern Territory")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r Capital}
Capital <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A5:AB30")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Count") %>%
mutate(State = "Capital")%>%
select(Offence,Year,Count,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery"," Unarmed robbery","Blackmail/extortion"))
```
```{r Capital_rate,message=FALSE}
Capital_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
pivot_longer(cols = '2010':'2019',
names_to = "Year",
values_to = "Rate") %>%
mutate(State = "Capital")%>%
select(Offence,Year,Rate,State)%>%
filter(Offence %in%c("Murder","
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```
```{r State}
State <- bind_rows(NewSouthWales,Victoria,Queensland,South_Australia,Western_Australia,Tasmania,Northern_Territory,Capital)
```
```{r}
State_rate <- bind_rows(NewSouthWales_rate,Victoria_rate,Queensland_rate,South_Australia_rate,Western_Australia_rate,Tasmania_rate,Northern_Territory_rate,Capital_rate)
```
```{r plot1,fig.height = 9, fig.width=10,fig.cap="Comparison of the number of victims of various crimes in each state in the same year"}
State %>%
ggplot(aes(x = Offence,
y = Count,
fill = State))+
geom_bar(stat = "identity", position = "dodge") +
ggtitle("Comparison of the number of victims of various crimes in each state in the same year") +
facet_wrap(~Year, ncol = 1,scales= "free")
```
Row {data-height=1000}
-------------------------------------
### Chart 2
```{r plot2,fig.height = 8, fig.width=8, fig.cap="Rate of change in the number of victims of various crimes by state"}
State %>%
ggplot(aes( x = Year,
y = Count,
color = Offence,
group = Offence)) +
geom_line(stat = "identity") +
theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
ggtitle("Rate of change in the number of victims of various crimes by state") +
facet_wrap(~State, ncol = 1,scales= "free")
```
### Chart 3
```{r}
State_count1<-State %>%
filter(Offence == "Sexual assault")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("Sexual assault_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("Sexual assault_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
State_count2<-State %>%
filter(Offence == "Murder")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("Murder_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("Murder_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
State_count3<-State %>%
filter(Offence == "Armed robbery")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("robbery_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("robbery_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
State_count4<-State %>%
filter(Offence == "Manslaughter")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("Manslaughter_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("Manslaughter_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
State_count5<-State %>%
filter(Offence == "Kidnapping/abduction")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("Kidnapping_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("Kidnapping_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
State_count6<-State %>%
filter(Offence == "Blackmail/extortion")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Count)%>%
mutate("extortion_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
mutate("extortion_change" = (`2019` - `2010`))%>%
select(!`2010`:`2019`)
```
```{r table1,fig.height = 4 , fig.width=6}
state_sum1<-merge(State_count1,State_count2, by= 'State')
state_sum<-merge(State_count3,state_sum1, by= 'State')
knitr::kable(state_sum,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
kable_styling(bootstrap_options = c("striped", "hover"))
```
```{r table2}
state_sum2<-merge(State_count4,State_count5, by= 'State')
state_sum3<-merge(State_count6,state_sum2,by= 'State')
knitr::kable(state_sum3,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
kable_styling(bootstrap_options = c("striped", "hover"))
```
Row {data-height=600}
-------------------------------------
### Chart 4
```{r plot3,fig.height = 5 ,fig.width=7,fig.cap="The rate of change in the rate of various crime victims by state"}
State_rate %>%
filter(Offence == "Sexual assault")%>%
ggplot(aes( x = Year,
y = Rate,
color = State,
group = State)) +
geom_line(stat = "identity") +
ylab("rate-Victims per 100,000")+
theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
ggtitle("Rate of change in the number of victims of various crimes by state")
```
### Chart 5
```{r table3}
State_rate1<-State_rate %>%
filter(Offence == "Sexual assault")%>%
pivot_wider(id_cols = State,
names_from = Year,
values_from = Rate)%>%
mutate("2010-2011" = (`2019` - `2010`))%>%
mutate("rate_change" = ((`2019` - `2010`)/`2010`)*100)%>%
select(!`2010`:`2019`)
knitr::kable(
State_rate1, booktabs = TRUE,
caption = 'A 10-year change in the proportion of victims of sexual assault')%>%
kable_styling(bootstrap_options = c("striped", "hover"))
```
Gender {data-icon="fa-user-plus"}
==================
Column{data-width=400}
--------
### Total number by gender {data-width=350}
```{r echo=FALSE}
Victims_of_Crime_raw <- readxl::read_excel("data/Victims_of_Crime_Australia.xls", sheet = 3, skip = 4, col_names = FALSE)
Victims_of_Crime <- Victims_of_Crime_raw[-1,1:11]
colnames(Victims_of_Crime) = Victims_of_Crime[1,]
Victims_of_Crime <- Victims_of_Crime[-1,]
Homicide <- Victims_of_Crime[-1,] %>%
slice(c(1:18)) %>%
filter(`Sex and age` == "Total")
Homicide[1,1] <- "Homicide_Male"
Homicide[2,1] <- "Homicide_Female"
Homicide[3,1] <- "Homicide_All"
Homicide <- Homicide %>% rename("Type_Gender" = `Sex and age`)
Murder <- Victims_of_Crime %>%
slice(c(21:38)) %>%
filter(`Sex and age` == "Total")
Murder[1,1] <- "Murder_Male"
Murder[2,1] <- "Murder_Female"
Murder[3,1] <- "Murder_All"
Murder <- Murder %>% rename("Type_Gender" = `Sex and age`)
Attemptedmurder <- Victims_of_Crime %>%
slice(c(40:57)) %>%
filter(`Sex and age` == "Total")
Attemptedmurder[1,1] <- "Attemptedmurder_Male"
Attemptedmurder[2,1] <- "Attemptedmurder_Female"
Attemptedmurder[3,1] <- "Attemptedmurder_All"
Attemptedmurder <- Attemptedmurder %>% rename("Type_Gender" = `Sex and age`)
Manslaughter <- Victims_of_Crime %>%
slice(c(59:76)) %>%
filter(`Sex and age` == "Total")
Manslaughter[1,1] <- "Manslaughter_Male"
Manslaughter[2,1] <- "Manslaughter_Female"
Manslaughter[3,1] <- "Manslaughter_All"
Manslaughter <- Manslaughter %>% rename("Type_Gender" = `Sex and age`)
Sexualassault <- Victims_of_Crime %>%
slice(c(78:110)) %>%
filter(`Sex and age` == "Total")
Sexualassault[1,1] <- "Sexualassault_Male"
Sexualassault[2,1] <- "Sexualassault_Female"
Sexualassault[3,1] <- "Sexualassault_All"
Sexualassault <- Sexualassault %>% rename("Type_Gender" = `Sex and age`)
Kidnappingabduction <- Victims_of_Crime %>%
slice(c(112:141)) %>%
filter(`Sex and age` == "Total")
Kidnappingabduction[1,1] <- "Kidnappingabduction_Male"
Kidnappingabduction[2,1] <- "Kidnappingabduction_Female"
Kidnappingabduction[3,1] <- "Kidnappingabduction_All"
Kidnappingabduction <- Kidnappingabduction %>% rename("Type_Gender" = `Sex and age`)
Robbery <- Victims_of_Crime %>%
slice(c(143:175)) %>%
filter(`Sex and age` == "Total")
Robbery[1,1] <- "Robbery_Male"
Robbery[2,1] <- "Robbery_Female"
Robbery[3,1] <- "Robbery_All"
Robbery <- Robbery %>% rename("Type_Gender" = `Sex and age`)
Armedrobbery <- Victims_of_Crime %>%
slice(c(177:209)) %>%
filter(`Sex and age` == "Total")
Armedrobbery[1,1] <- "Armedrobbery_Male"
Armedrobbery[2,1] <- "Armedrobbery_Female"
Armedrobbery[3,1] <- "Armedrobbery_All"
Armedrobbery <- Armedrobbery %>% rename("Type_Gender" = `Sex and age`)
Unarmedrobbery <- Victims_of_Crime %>%
slice(c(211:243)) %>%
filter(`Sex and age` == "Total")
Unarmedrobbery[1,1] <- "Unarmedrobbery_Male"
Unarmedrobbery[2,1] <- "Unarmedrobbery_Female"
Unarmedrobbery[3,1] <- "Unarmedrobbery_All"
Unarmedrobbery <- Unarmedrobbery %>% rename("Type_Gender" = `Sex and age`)
Blackmailextortion <- Victims_of_Crime %>%
slice(c(245:274)) %>%
filter(`Sex and age` == "Total")
Blackmailextortion[1,1] <- "Blackmailextortion_Male"
Blackmailextortion[2,1] <- "Blackmailextortion_Female"
Blackmailextortion[3,1] <- "Blackmailextortion_All"
Blackmailextortion <- Blackmailextortion %>% rename("Type_Gender" = `Sex and age`)
Victims_of_Crime_tidy <- bind_rows(Homicide, Murder, Attemptedmurder, Manslaughter, Sexualassault, Kidnappingabduction, Robbery, Armedrobbery, Unarmedrobbery, Blackmailextortion) %>%
mutate(`2010` = as.numeric(`2010`)) %>%
pivot_longer(cols = -Type_Gender,
names_to = "Year",
values_to = "Number") %>%
separate(col = Type_Gender,
into = c("Type", "Gender"), "_") %>%
filter(!Type == "Homicide",
!Type == "Robbery")
```
```{r echo=FALSE}
g <- Victims_of_Crime_tidy %>%
group_by(Year, Gender) %>%
summarise(sum = sum(Number)) %>%
ggplot(aes(x = Year,
y = sum,
color = Gender,
group = Gender)) +
geom_line() +
theme_bw() +
scale_color_brewer(palette = "Dark2") +
theme(legend.position = "bottom")
ggplotly(g)
```
### Analysis{data-width=250}
* On the whole, it shows an upward trend year by year recently.
* The number of female victims is much higher than that of male victims, almost twice.
* With the exception of sexual assault and kidnapping, the majority of victims are men.
* The vast majority of cases are sexual assaults.
* The number of robberies among the remaining crime categories is also considerable. (Including both armed and unarmed)
* Possible reasons:
+ Women generally have stronger safety awareness
+ Women are less likely to be seen alone at night in areas where they are likely to be robbed
According to statistics of gender-specific crimes in various countries, male victims are the majority in almost all crimes except for sex-related crimes in which more women are victims.
Column{data-width=400}
---------------
### Number by gender by crime type {data-width=400}
```{r fig.height = 8, echo=FALSE}
Victims_of_Crime_tidy %>%
ggplot() +
geom_col(aes(x = Year,
y = Number,
fill = Gender),
position = "dodge") +
facet_grid(Type~., scales = "free_y") +
scale_fill_brewer(palette = "Dark2") +
theme_bw()
```
### Sexual assault proportion {data-width=450}
```{r echo=FALSE}
Victims_of_Crime_sum <- Victims_of_Crime_tidy %>%
filter(!Gender == "All") %>%
group_by(Gender, Year) %>%
summarise(sum = sum(Number))
Victims_of_Crime_tidy %>%
filter(Type == "Sexualassault" &
!Gender == "All") %>%
left_join(Victims_of_Crime_sum) %>%
group_by(Gender, Year) %>%
summarise(sa_rate = Number/sum*100) %>%
kable(caption = "Sexual Assault Rate") %>%
kable_styling(bootstrap_options = c("striped", "hover"))
```
Take sexual assault, female victims of sexual assault make up a very high proportion of total female victims.
Column{data-width=400}
-----------
### Rate by gender{data-width=350}
```{r echo=FALSE}
Victims_of_Crime1 <- Victims_of_Crime_raw[-1,c(1, 12:21)]
colnames(Victims_of_Crime1) = Victims_of_Crime1[1,]
Victims_of_Crime1 <- Victims_of_Crime1[-1,]
Sexualassault1 <- Victims_of_Crime1 %>%
slice(c(78:110)) %>%
filter(`Sex and age` == "Total")
Sexualassault1[1,1] <- "Sexualassault_Male"
Sexualassault1[2,1] <- "Sexualassault_Female"
Sexualassault1[3,1] <- "Sexualassault_All"
Sexualassault1 <- Sexualassault1 %>% rename("Type_Gender" = `Sex and age`)
Sexualassault_rate <- Sexualassault1 %>%
mutate(`2010` = as.numeric(`2010`)) %>%
pivot_longer(cols = -Type_Gender,
names_to = "Year",
values_to = "Rate") %>%
separate(col = Type_Gender,
into = c("Type", "Gender"), "_") %>%
filter(!Gender == "All")
```
```{r echo=FALSE, fig.height=3, fig.width=5}
sa <- Sexualassault_rate %>%
ggplot() +
geom_col(aes(x = Year,
y = Rate,
fill = Gender),
position = "dodge") +
theme_bw() +
scale_fill_brewer(palette = "Dark2") +
theme(legend.position = "bottom")
ggplotly(sa)
```
### Analysis
* Rate is calculated by victim number divided by 100,000 persons.
* Show an upward pattern over years.
* Higher than 0.15% in recent years which means 3 out of every 2,000 people have been sexually assaulted.
* Possible reasons:
+ Women are physically vulnerable
+ Men are more sexually impulsive
+ Many women may choose not to report the crime
- relationship to the offender
- confidence in the justice system
- fear of revenge
- public opinion
ABS personal safety survey also measures the number of women who contacted the police about the most recent incident within the last 10 years. Only 13.4% of women did so.
Age {data-icon="fa-user-times"}
====================